Owing to the huge mass of content available over the World Wide Web, end users accessing content provided by service providers are often provided personalized assistance and data analytics by the service providers, search engines, web publishers, and advertisers for easily accessing relevant content. Conventionally, various techniques, such as content based recommendation, collaborative recommendation, and data analytics, are used to provide personalized services to the end users. In content based recommendation, the end users are recommended content, services or products which are similar to the content, services or products used or liked by the end users in the past or which match the interest or choice of the end user. In collaborative recommendation, the end user is recommended content, services or products which are similar to the content, services or products used or liked by other end users having similar or same interest or choices.
In an example of content based recommendation, a movie review website may monitor an end user to regularly view a certain category of movies, for example, animated movies. Accordingly, every time an animated movie is available for view, the end user may be provided a recommendation, such as a notification or an alert, for example, to download the movie by making relevant payments. Similarly, in collaborative recommendation, also known as collaborative filtering, service providers may provide targeted advertisements to an end user where these advertisements pertain to product or services that have been preferred by other end users who have similar interests and preferences as the end user. For example, an internet protocol television (IPTV) service provider may recommend television shows or movies to the end user, if the television shows or movies have been viewed by other end users whose interests match the interests of the end user.
Further, users need to provide their personnel data to service providers for executing data analytics applications, for example, for data mining purposes. In midst of all these techniques for data analytics and offering relevant content to the end users by the service providers, users of today feel increasingly concerned about their personal and potentially sensitive information. This is mainly because in order to benefit from the personalized services and facilitate data analytics, the end users have to reveal sensitive information, but at the same time, they are concerned about the privacy protection of the information. For example, an end user of a social networking site may not object to the use of the information of his accessing social networking sites to make anonymous recommendations to other end users and to himself regarding updates on social networking sites as such, but the end user may not want the other entities, such as other end users, the service providers, attackers and malicious parties, to know the particular URLs that the end user visited or rated.